Senior Model Risk Manager - Ai/ml

Mercury Mercury · Fintech · Remote · Policy & Government Affairs

This role is responsible for defining and enhancing model risk management (MRM) frameworks for AI/ML systems at Mercury, a fintech company. The Senior Model Risk Manager will own the validation, monitoring, and governance of the AI/ML model portfolio, including predictive models, generative AI systems, and agentic workflows. The role requires a strong understanding of AI risks, partnering with various teams, and shaping responsible AI standards. The position also involves developing AI-enabled automation tools for MRM and championing MRM as a strategic enabler.

What you'd actually do

  1. Maintain and enhance Mercury’s model governance framework, including inventory standards, documentation templates, validation standards, and issue management.
  2. Perform independent validation across predictive ML models, generative AI systems, and agentic workflows, covering data, assumptions, methodology, testing, and monitoring.
  3. Assess risks in LLM-powered applications, including RAG pipelines, tool use, autonomy boundaries, human oversight, and hallucination risk.
  4. Serve as a trusted advisor to data scientists, engineers, product teams, and risk partners throughout the AI/ML lifecycle to provide practical guidance on model risk, governance expectations, and control design without slowing responsible innovation.
  5. Develop and maintain AI-enabled automation tools to improve the speed, scale, and effectiveness of model governance and validation workflows.

Skills

Required

  • Python
  • SQL
  • scikit-learn
  • XGBoost
  • LLMs
  • RAG systems
  • prompt engineering
  • AI agent frameworks
  • evaluating and testing machine learning models
  • evaluating and testing generative AI systems
  • custom evals
  • red-teaming
  • model risk governance principles
  • regulatory expectations
  • model governance
  • independent effective challenge
  • documentation
  • quantitative analysis
  • written and verbal communication skills

Nice to have

  • traditional model validation background
  • model development
  • applied AI
  • research as data scientists
  • financial services or fintech experience

What the JD emphasized

  • No one has fully solved this yet.
  • continuously building and enhancing the frameworks, not just inheriting them
  • rigorously challenge them as they scale into production
  • independent validation
  • Assess risks in LLM-powered applications
  • emerging AI risks
  • without slowing responsible innovation
  • Evaluate new AI use cases for regulatory implications
  • AI-enabled automation tools
  • maintaining strong governance standards
  • rigorous, forward-looking MRM on AI
  • ideal candidates may come from a traditional model validation background with deep hands-on experience testing modern AI/ML systems, or from model development, applied AI, or research as data scientists, with a strong understanding of how risks emerge in complex systems and how to rigorously challenge them as they scale into production.
  • familiarity with LLMs, RAG systems, prompt engineering, and AI agent frameworks.
  • Experience in evaluating and testing machine learning models (e.g. in fraud detection) and generative AI systems, including custom evals, red-teaming, or frameworks.
  • Solid understanding of model risk governance principles and regulatory expectations (e.g. SR 11-7 / OCC 2011-12, SR 26-2).
  • Deep appreciation of disciplined model governance and independent effective challenge.
  • Comfort operating in ambiguity
  • High agency and adaptability

Other signals

  • defining model governance for AI/ML
  • validation, monitoring, and governance of AI/ML model portfolio
  • thought leader in MRM evolution for AI
  • partnering with data scientists, engineers, compliance, and product teams
  • shaping Mercury's responsible AI standards